Producers are increasingly compelled by legislation to understand the volume of their products recycled and a product's total impact on waste management systems (WMS). Improved data quality is essential to drive the adoption of a waste management system, and to guide policy strategy and planning decisions. This study focuses on the data from post-consumer waste in the City of Cape Town within the broad field of solid waste management (SWM). This study explores the (primary) research questions: “What is the value of waste data in the City of Cape Town waste management system ?” It answer the primary question through three secondary-questions 1. How do stakeholders within the WMS perceive the availability of waste data? 2. What is the current volume and composition of waste data in the City Of Cape Town's WMS ? 3. Additionally, can data from the informal waste sector assist in improving existing waste data quality? A sequential mixed-methods exploratory case study approach and the solid waste information management model (SWIMM) are used. Through interviews with key industry players, a review of selected literature, and publicly available data on waste from the City of Cape Town, the study surfaces the misalignment between stakeholder expectations and currently collected data on waste within the waste management system. This study finds that expectations from stakeholders across waste management are misaligned around the perception of essential waste data indicators. The research presents a novel SWIMM model that collects a higher volume, frequency, quality, diversity, and composition of data across the waste stream, predicting future short-term waste scenarios, legislation compliance, and production planning.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:uct/oai:localhost:11427/37801 |
| Date | 20 April 2023 |
| Creators | Stemmet, Paulus |
| Contributors | Uys, Walter, Rivett, Ulrike |
| Publisher | Faculty of Commerce, Department of Information Systems |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Master Thesis, Masters, MCom |
| Format | application/pdf |
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